Introduction Existing systems estimate user price sensitivity primarily from spending behavior or demographic proxies. They do not systematically account for residential property values, which can indicate a user’s financial circumstances. This omission creates three limitations: Limited property insights: Existing profiles do not account for property values. One-dimensional profiles: Users with similar spending patterns but different living standards receive the same classification. Regional variation: Manual classification does not adapt well to differences between property markets. This…
The short story Consider a Friday evening. A food order arrives from a mall in the city center. One driver is nearby; another is finishing a drop-off and will be available shortly; a second order from the same mall may or may not appear in the next two minutes. Dispatch the nearby driver now, or hold briefly for a batching opportunity? The decision window is short. A fulfillment marketplace makes these decisions continuously. Each one is small. Across a city, those decisions determine whether your dinner arrives hot and whether a driver’s hour is well spent. And that is one decision. There…
Introduction Merchant reviews contain useful details about food quality, portion size, packaging, and value. Finding these details often requires reading many comments. Aggregate ratings simplify comparisons, but they do not explain what shaped each score. We built the Automated Merchant Review Summary System to turn written reviews into concise summaries. Consumers can scan an overall summary or focus on a specific topic. Merchants can identify what customers value and which parts of the experience need attention. This article focuses on how we generate and maintain consumer-facing review…
Introduction In the first two parts of this series, we described how Grab approaches data mesh through the Signals Marketplace: a way for teams to publish, discover, and reuse trusted data products across domains. Part II introduced the foundational tools behind certification: Hubble for metadata and ownership, Genchi for data quality observability, and the Data Contract Registry for explicit producer-consumer guarantees. Certification is the starting point for a trusted data marketplace. It gives downstream consumers confidence in an asset’s ownership, documentation, lineage, and quality…
Introduction The first Jarvis Pro prototype could produce answers that sounded right. That was the problem. One early answer looked polished: it named the merchant, summarized the week, and recommended pushing promotions before the next review. It was also wrong. The merchant’s order volume was down, but the sharper issue was operational: more outlets were paused and fulfilment had slipped. Sending more demand into that setup would have made the merchant look worse. That failure changed how we judged the system. Fluent was not enough. Jarvis Pro is the AI assistant we built for Grab account…
Introduction What worried us wasn’t the hallucination, it was the subtle plausibility. Answers an engineer could easily read past and accept: a right-looking Structured Query Language (SQL) query, a plausible tool call, an innocent profile update, or a patch that satisfied the surface tests. When we analyzed the row-level failures, a clear pattern emerged: SQL generation: kept the query shape but changed the underlying metric. Tool calling: selected the right tool family but drifted on parameters. Profile updates: cited every event instead of only the evidence that supported the claim. Coding…
Introduction At Grab, analytics sits close to almost every decision that matters. Our north star is the democratisation of intelligence, ensuring that anyone making a business call has immediate access to trustworthy answers. Over the last two years, model capability has crossed a threshold enabling this shift. Agents now do in minutes what used to take a week: preparing the data, writing queries, running deep analysis and developing insights for business opportunities, designing experiments and interpreting the results, drafting the commentary that follows, and more. Our throughput is no…
Introduction The efficacy of semantic search relies on the accuracy of the underlying Knowledge Graph (KG). In high-velocity domains like on-demand food delivery or e-commerce, the catalog of entities like dishes, products, and merchants changes rapidly. Current methods for KG construction and maintenance face three critical challenges: Inaccuracy and hallucination from Large Language Models (LLMs): Automated models often infer relationships based on statistical text co-occurrence rather than semantic reality. For instance, an LLM might incorrectly classify “Pho” as a child of “Italian Noodle…
Part 1: From one support bot to a framework At Grab, AI agents have evolved from interesting team prototypes into production services used every day by millions of merchants, drivers, and consumers. Today, more than 500 services run on our internal agent framework, over 50 Model Context Protocol (MCP) servers are registered on our remote MCP framework, and a single Large Language Model (LLM) gateway fronts every model call across the company, handling billions of tokens each month. None of this was designed up front. It began as the plumbing behind one internal support bot, which then…
Introduction: The evolution of Grab’s Data Lake At Grab’s scale, managing petabytes of data across billions of S3 objects demands more than a storage layer. It demands a robust architectural primitive that supports the high-concurrency needs of a modern “Lakehouse.” Our goal is full storage-compute separation, leveraging S3 as an elastic foundation for both near-real-time metrics and large-scale batch transformations. For years, the vast majority of our tables were Hive Parquet, managed through the Hive Metastore with a directory-based layout. This model served us well, but as data volume…
Introduction Counter Service is used across Grab’s anti-fraud platform to answer time-windowed count questions, such as recent ride requests by a user or failed payment attempts on a card. The service handles tens of thousands of queries per second (QPS) with about a billion requests per day, while maintaining strict requirements around latency and reliability to support real-time fraud rule evaluation. For most of its life, Counter Service was backed by a wide-column database that served the workload reliably as the service scaled. As part of a broader infrastructure review mandated at an…
Distroless adoption at Grab Grab is migrating from heavy base images to Distroless images to reduce security risks. By limiting each container to the application and its runtime dependencies, we shed non-essential binaries and associated Common Vulnerabilities and Exposures (CVEs). This migration is more than a compliance mandate; it is a strategic security decision to build a more resilient environment. Why Distroless requires rigorous testing Distroless adoption risk: Runtime failure Shifting to Distroless images introduces a critical technical risk: Runtime Failure. A service might build…
Introduction In Part 1, we introduced Palana, Grab’s Kubernetes-native secure execution platform for autonomous AI agents. We discussed the underlying need for isolated environments and covered its core design principles: treating isolation as the unit of trust, keeping credentials out of agent hands, and mediating all network access. In this second part, we’ll dive under the hood into Palana’s architecture, look at the agent lifecycle, and share the key lessons we learned from putting this system into production. Architecture overview The core request path looks like this: Figure 1. Palana…
Read at the source
Your visit, your choice.
Optional Google Analytics helps us understand visits. Microsoft Clarity records masked interactions to improve the site. Optional tools stay off unless you choose them. Privacy details.